Identification of genes with clonal mutations for the tumorigenesis of breast cancer subtypes.

Abstract

e13006 Background: Current analyses on somatic mutations mainly involve counting samples as either carriers or non-carriers. Such a binary approach misses to take into account the fraction of tumor cells that carry a mutation, which is a significant measure of the influence of a mutation on the overall phenotype, such as gene expressions, subtype and pathway activity. Methods: In this study, we re-analyzed the 72,084 non-synonymous somatic mutation in 16,164 genes of 817 TCGA breast cancer samples (HER2+: 65, Luminal A: 415, Luminal B: 176, Basal: 136 and Normal-like: 25) using variant allele frequency (VAF) adjusted by sample purity derived from multiple methods. We obtained a gene-based VAF by choosing the maximum VAF among all mutations found in each gene for each sample. For each breast cancer subtype, we filtered for top 100 genes with highest number of mutation carriers and then ranked them by decreasing average VAF across all samples. We assume genes with higher average VAF are more likely to harbor clonal mutations in early tumor progression. We further evaluated the Pearson correlation between VAF of genes having at least ten mutation carriers and the expression levels of ESR1, PGR and ERBB2 in Luminal A subtype. Results: The top two genes with highest VAF for each of the four subtypes are as follows: (a) HER2+: ERBB2 (VAF = 0.39 ± 0.21, n = 4), KAT6A (0.31 ± 0.09, 4), (b) Luminal A: CTCF (0.41 ± 0.19, 13), MAP2K4 (0.37 ± 0.14, 24), (c) Luminal B: MAP2K4 (0.46 ± 0.11, 5), TP53 (0.46 ± 0.17, 64), and (d) Basal: SCN10A (0.46 ± 0.29, 5), MYH9 (0.43 ± 0.16, 5). The strongest correlations for Luminal A are: (i) SPEN (corr. = 0.85, n= 14), KMT2C (0.73, 24) and DMD (0.73, 11) with ESR1, (ii) DMD (0.69, 11) and NBL1 (0.67, 10) with PGR, and (iii) TBX3 (-0.51, 12) and MUC12 (0.47, 14) with ERBB2. Conclusions: While carrier count is effective for identifying genes prone to mutations, average VAF opens another perspective for uncovering genes that tend to harbor clonal mutations. Our work shows the potential of VAF analysis for identifying driver genes, understanding tumor progression and evaluating the impact of a mutation on a patient. As future work, we may improve the VAF estimates by adjusting for copy number variations and weight each variant by pathogenicity.